The present invention relates to a system for coding images, and more particularly, to a system for compressing images to a reduced number of bits by employing a Discrete Cosine Transform (DCT) in combination with a visual model.
There has been significant development in the compression of digital information for digital images. The effective compression of digital information is important to maintain sufficient quality of the digital image while at the same time reducing the amount of data required for representing the digital image. The transmission of the digital images has gained particular importance in television systems and Internet based transmission. If the digital images include a relatively large number of bits to represent the digital images, a significant burden is placed on the infrastructure of communication networks involved with the creation, transmission, and re-creation of digital images. For this reason, there is a need to compress digital images to a smaller number of bits, by reducing redundancy and “invisible” image components of the images themselves.
Still image compression techniques, such as JPEG, compress digital information for digital images. As in digital compression for the transmission of digital video, JPEG compression includes a tradeoff between file size and compressed image quality. For example, JPEG compression is extensively used in digital cameras, Internet based applications, and databases containing digital images.
Many of the image compression techniques, such as JPEG and MPEG, include a transform coding algorithm for the digital image, wherein the image is divided into blocks of pixels. For example, each block of pixels may be an 8×8 or 16×16 block of pixels. Each block of pixels then undergoes a two dimensional transform to produce a two dimensional array of transform coefficients. For many image coding applications, a Discrete Cosine Transform (DCT) is utilized to provide an orthogonal transform. After the block of pixels undergoes a Discrete Cosine Transform (DCT), the resulting transform coefficients are subject to compression by thresholding and quantization operations. Thresholding involves setting all coefficients whose magnitude is smaller than a threshold value equal to zero, whereas quantization involves scaling a coefficient by step size and rounding off to the nearest integer.
Commonly, the quantization of each DCT coefficient is determined by an entry in a quantization matrix (Q-table). A quantization matrix includes a plurality of values that is used to group a set of values together. For example, a quantization matrix may be used to group the values from 0 to 3 into group 1, values from 3-6 into group 2, and values from 6-9 into group 3. It is this matrix that is primarily responsible for the perceived image quality and the bit rate of the transmission of the image. The perceived image quality is important because the human visual system can only tolerate a certain amount of image degradation without significantly observing a noticeable error. Therefore, certain images can tolerate significant degregration and thus be significantly compressed, whereas other images cannot tolerate significant degradation and should not be significantly compressed.
Some systems include computing a single DCT quantization matrix based on human sensitivity. One such system is based on a mathematical formula for the human contrast sensitivity function, scaled for viewing distance and display resolution, as taught in U.S. Pat. No. 4,780,716. Another such system is based on a formula for the visibility of individual DCT basic functions, as a function of viewing distance, display resolution, and display luminance. The formula is disclosed in both a first article entitled “Luminance-Model-Based DCT Quantization For Color Image Compression” of A. J. Ahumada et al. published in 1992 in the Human Vision, Visual Processing, and Digital Display III Proc. SPIE 1666, Paper 32, and a second technical article entitled “An Improved Detection Model for DCT Coefficient Quantization” of H. A. Peterson, et al., published in 1993, in Human Vision, Visual Processing and Digital Display VI Proc. SPIE. Vol. 1913 pages 191-201. The techniques described in the '761 patent and the two technical articles do not adapt the quantization matrix to the image being compressed.